The classification of high-resolution data is becoming increasingly important in a wide variety of applications. For example, in medical applications, such as digital pathology, image classification can be applied to high-resolution CT scan or MRI images in order to detect and diagnose numerous diseases, illnesses or other conditions.
Other examples of high-resolution image classification applications include security applications involving image or face recognition, oil and gas exploration applications involving analysis of geological images, and astrophysics applications involving analysis of large-scale imagery of galaxies and molecular clouds.
Unfortunately, conventional techniques for image classification suffer from a number of significant drawbacks. For example, image classification in many cases remains unduly human intensive. This is particularly true in digital pathology, where millions of medical images are manually analyzed for classification purposes every year by specialists and other medical professionals. These experts study medical images and try to identify patterns they have seen before. However, there is generally no mechanism available that leverages previous image analyses across multiple classifications in an accurate and efficient way. Instead, the results of image classifications performed by various experts may remain scattered across unrelated processing systems.
Existing medical image processing approaches therefore involve creating and reviewing high-resolution images, such as images generated by CT scans and MRIs, as a one-time event to aid in diagnosing a condition at a given point in time. Because these images are so intensive with regard to the size of the images and high-resolution medical imaging requires significant space to store and manage, they are archived to tape periodically and are not readily available for subsequent analysis to help diagnose future, similar medical conditions.
Similar analysis problems exist in other high-resolution data classification applications. Conventional approaches fail to adapt the classification process based on data-specific factors or past classification history.
Accordingly, a need exists for improved data classification techniques that can adapt more readily over time and are better suited to implementation in parallel processing arrangements.